Hadi Meidani

About

Hadi Meidani is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on transforming how engineering systems are modeled, designed, and operated by advancing a new paradigm of AI-driven scientific computing. His work includes physics-informed machine learning, neural operators, and graph-based AI models to accelerate traditional simulation and enable scalable digital twins for infrastructure systems, transportation, structural mechanics, and biomedical applications. Dr. Meidani has received recognition such as an NSF CAREER Award for his contributions to fast computational models for infrastructure networks. His team has won awards from data competitions related to railroad engineering, and his research has been sponsored by federal agencies including NSF, DOE, and DOT. Prior to joining UIUC, he held postdoctoral positions at USC and the University of Utah, and he is the Chair of the Machine Learning Committee of the ASCE Engineering Mechanics Institute.

Research topics

• Artificial Intelligence
• Computer Science
• Mathematics
• Machine Learning
• Algorithm
• Applied mathematics
• Mathematical analysis
• Mathematical optimization

Selected publications

• Efficient training of physics‐informed neural networks via importance sampling

  Computer-Aided Civil and Infrastructure Engineering · 2021 · 282 citations

  Senior authorCorresponding
  DOI

• PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations

  Computer Methods in Applied Mechanics and Engineering · 2022 · 51 citations

  Senior authorCorresponding
  DOI

• Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems

  Journal of Infrastructure Systems · 2024-08-21 · 30 citations

  articleSenior author

  Rapid reliability assessment of transportation networks can enhance preparedness, risk mitigation, and response management procedures related to these systems. Network reliability analysis commonly considers network-level performance and does not consider the more detailed node-level responses due to computational cost. In this paper, we propose a rapid seismic reliability assessment approach for bridge networks based on graph neural networks, where node-level connectivities, between points of i…

  PublisherDOI

• Physics-Informed Geometry-Aware Neural Operator

  Computer Methods in Applied Mechanics and Engineering · 2024-11-26 · 23 citations

  articleOpen accessSenior authorCorresponding

  Engineering design problems often involve solving parametric Partial Differential Equations (PDEs) under variable PDE parameters and domain geometry. Recently, neural operators have shown promise in learning PDE operators and quickly predicting the PDE solutions. However, training these neural operators typically requires large datasets, the acquisition of which can be prohibitively expensive. To overcome this, physics-informed training offers an alternative way of building neural operators, eli…

  PublisherDOI

• FO-PINN: A First-Order formulation for Physics-Informed Neural Networks

  Engineering Analysis with Boundary Elements · 2025-02-25 · 21 citations

  articleOpen accessSenior authorCorresponding

  Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorporating the governing partial differential equations (PDEs) in their loss function. While PINNs are successfully used for solving forward and inverse problems, their accuracy decreases significantly for parameterized systems and higher-order PDE problems. PINNs also have a soft implementation of boundary conditions resul…

  PublisherDOI

Recent grants

• CAREER: Efficient Predictive Modeling for Infrastructure Systems Using Polynomial Approximation

  NSF · $500k · 2018–2024

Frequent coauthors

• Christopher W. Tessum

  University of Illinois Urbana-Champaign

  26 shared

• Sotiria Koloutsou‐Vakakis

  University of Illinois Urbana-Champaign

  26 shared

• Eleftheria Kontou

  University of Illinois Urbana-Champaign

  26 shared

• Lei Zhao

  25 shared

• Roger Ghanem

  14 shared

• Negin Alemazkoor

  12 shared

• Mohammad Amin Nabian

  Nvidia (United States)

  12 shared

• Weiheng Zhong

  10 shared

Labs

• Computational Intelligence for Engineering Lab (CIEL)PI

Education

• Ph.D., Computer Science

  University of Illinois at Urbana-Champaign

  2002

• M.S., Computer Science

  University of Illinois at Urbana-Champaign

  1998

• B.S., Computer Engineering

  University of Tehran

  1995

Awards & honors

• NSF CAREER Award on fast computational models for infrastruc…

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